Journal of Education Finance Pub Date : 2021-09-24 Bruce D. Baker, Mark Weber, Ajay Srikanth
- Informing Federal School Finance Policy with Empirical Evidence
- Bruce D. Baker, Mark Weber, and Ajay Srikanth
introduction
This article explains the process behind estimating a National Education Cost Model (NECM) and generating from that model projections of per-pupil costs to achieve 2016 national average outcomes (reading and math grades 3 to 8) across all districts in the United States, from 2019–20 to 2020–21. This article is a follow-up to a preliminary report prepared in 2018 in which we made our first attempts to estimate a National Education Cost Model using methods most often applied to individual states (Baker, Weber, Srikanth, Atzbi, Kim, 2018). Here, we have expanded on this process, to develop a three-step method to take us from an estimated cost model using district level data on approximately 10,000 districts per year from 2009 to 2016, to a simulation of estimated costs for over 13,000 districts for 2019 through 2021. That process involves the following steps to be explained in detail in this article:
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Step 1: Estimating a National Education Cost Model to historical district level panel data with missing data;
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Step 2: Estimating a set of parsimonious weighting factors that approximate the cost model estimates with more easily updated, limited measures, covering all districts;
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Step 3: Developing a formula simulation to apply to current and future district level data, complete panel.
From this formula simulation, we compare the most recent years of district level actual spending reports (fiscal year 2017) to what would be needed for children in each district to have equal opportunity to achieve a given outcome goal. Here, that modest goal, is to raise the national floor to the national average of past years.
Statistical modeling of the type used herein yields estimates. These estimates are imperfect but useful, yest one must be careful not to overinterpret these estimates, or assume them to be exact or perfect targets for the amount of [End Page 1] money that must be spent to precisely achieve a selected outcome. The goal of education cost modeling, whether for evaluating equal educational opportunity or for producing adequacy cost estimates, is to establish reasonable guideposts for developing more rational school finance systems. To summarize, the goals and advantages of the approach provided herein are:
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• Cost model estimates provide reasonable marks, where previously there were none.
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• Specifically, they provide estimates related to common outcome goals, which was not previously possible.
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• These marks can guide policy but may not necessarily dictate it.
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• Introducing this evidence into deliberations over a new federal aid formula can help to “bend” public policy, specifically federal aid distribution formulas, in a better direction than if such evidence did not exist or was simply ignored.
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• Ultimately, the goal of introducing rigorous empirical evidence on education costs (tied to outcomes) into formula deliberations is to achieve an end result (from the necessarily political process) that is “less bad than it might otherwise be.”
Statistical cost modeling is the most appropriate method for understanding education costs, cost variation across children and settings, toward achieving a commonly measured outcome goal. Back in 2004, economist Thomas Downes of Tufts explained (in a review of cost analysis methods), that “Given the econometric advances of the last decade, the cost-function approach is the most likely to give accurate estimates of the within-state variation in the spending needed to attain the state’s chosen standard, if the data are available and of a high quality” (p. 9). Significant advances in data quality, statistical computing and econometric techniques since 2004 have improved education cost modeling (Duncombe and Yinger, 2011). The primary objective of this exercise is to better understand the variation in costs toward common, measured outcome goals.
Downes focused on “within-state” variation, because, at the time researchers lacked the ability to compare outcomes of districts across states. With the release and updating of the Stanford Education Data Archive (SEDA), we now have eight years of nationally equated district level reading and math scores, grades 3 to 8. We also have a rich archive of district level fiscal, economic context and student enrollment data in the School Finance Indicators Database (SFID). Finally, two new sources of useful...
中文翻译：
用经验证据为联邦学校财政政策提供信息
代替摘要，这里是内容的简短摘录：
- 用经验证据为联邦学校财政政策提供信息
- Bruce D. Baker、Mark Weber 和 Ajay Srikanth
介绍
本文解释了估算国家教育成本模型 (NECM) 以及从该模型预测每个学生的成本以实现美国所有地区 2016 年全国平均成绩（阅读和数学 3 至 8 年级）背后的过程，从2019-20 至 2020-21。本文是 2018 年准备的初步报告的后续报告，在该报告中，我们首次尝试使用最常应用于各个州的方法来估算国家教育成本模型（Baker、Weber、Srikanth、Atzbi、Kim，2018 年）。在这里，我们扩展了这个过程，开发了一种三步法，使我们从使用 2009 年至 2016 年每年大约 10,000 个地区的地区级数据的估计成本模型，到模拟超过 13,000 个地区的估计成本。 2019 年至 2021 年。
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步骤 1：根据缺失数据的历史地区级面板数据估算国家教育成本模型；
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第 2 步：估计一组简约的加权因子，以更容易更新、有限的措施来近似成本模型估计，涵盖所有地区；
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第 3 步：开发适用于当前和未来地区级数据的公式模拟，完成面板。
从这个公式模拟中，我们将最近几年的地区级实际支出报告（2017 财年）与每个地区的儿童需要什么才能有平等的机会实现给定的结果目标。在这里，这个适度的目标是将全国底线提高到过去几年的全国平均水平。
此处使用的类型的统计建模产生估计。这些估计是不完美的，但有用的，yest必须要小心，不要过度解读这些估计，或承担他们是对的量准确或完善目标[尾页1]必须花费精确实现选择的结果钱。教育成本建模的目标，无论是评估平等教育机会还是进行充分成本估算，都是为发展更合理的学校财务系统建立合理的路标。总而言之，本文提供的方法的目标和优点是：
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• 成本模型估计提供了合理的分数，而以前没有。
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• 具体而言，它们提供了与以前不可能实现的共同结果目标相关的估计值。
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• 这些标记可以指导政策，但不一定是决定性的。
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• 将此证据引入新的联邦援助公式的审议中有助于“弯曲”公共政策，特别是联邦援助分配公式，使其朝着更好的方向发展，而不是这样的证据不存在或被简单地忽略。
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• 最终，在公式审议中引入关于教育成本（与结果相关）的严格经验证据的目标是实现一个最终结果（来自必然的政治过程）“不那么糟糕”。
统计成本模型是了解教育成本、不同儿童和环境的成本变化以实现共同衡量的结果目标的最合适方法。早在 2004 年，塔夫茨大学的经济学家托马斯·唐斯（Thomas Downes）解释说（在回顾成本分析方法时），“鉴于过去十年计量经济学的进步，成本函数方法最有可能给出州内变化的准确估计如果数据可用且质量高，则达到州选择的标准所需的支出”（第 9 页）。自 2004 年以来，数据质量、统计计算和计量经济学技术的重大进步改善了教育成本模型（Duncombe 和 Yinger，2011 年）。此练习的主要目标是更好地了解成本与常见的、可衡量的结果目标之间的差异。
唐斯专注于“州内”变化，因为当时研究人员缺乏比较各州地区结果的能力。随着斯坦福教育数据档案 (SEDA) 的发布和更新，我们现在拥有 8 年全国等同的地区级阅读和数学成绩，3 至 8 年级。我们还拥有丰富的地区级财政、经济背景和学生档案。学校财务指标数据库 (SFID) 中的入学数据。最后，两个新的有用来源......
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